Packages

• we will start using the pacman package instead of install.packages() and library
  • the function p_load() takes package names as arguments
  • then checks if you have the package installed
    • if you do –> loads the package (just like library())
    • if you don’t –> the package is installed and then loaded (like using install.packages() + library())
• this saves us from having to install new packages individually

CSV: Comma separated value

• there are many different file types that data can take, e.g., .xlsx, .txt, .csv, .tsv.

• csv is the most typical data file type, and stands for: Comma Separated Values.

• this is what a simple CSV file looks like when viewed as raw text

Student ID,Full Name,favourite.food,mealPlan,AGE
1,Sunil Huffmann,Strawberry yoghurt,Lunch only,4
2,Barclay Lynn,French fries,Lunch only,5
3,Jayendra Lyne,N/A,Breakfast and lunch,7
4,Leon Rossini,Anchovies,Lunch only,
5,Chidiegwu Dunkel,Pizza,Breakfast and lunch,five
6,Güvenç Attila,Ice cream,Lunch only,6

• the first row (the “header row”) contains the columns names
• the subsequent rows contain the data
• How many variables are there? how many observations?

Tidy data

• you want your data to be tidy
  • tidy data is rectangular, and:
  • each column represents a variable
  • each row an observation
  • each cell a data point (Figure 1).

Figure 1: Source: Wickham et al. (2023) (all rights reserved)

Spreadsheet to csv

• let’s collect some toy data in a spreadsheet which we will then save as a CSV file and load into R
  • Click here to go to an editable spreadsheet
  • Enter the relevant information about yourself, or make up some data: the name of a pet you have/had, height, birth month and day, plus your first language. If you have no pet, leave the cell blank.

Our spreadsheet

Save csv

• save the spreadsheet as groesse_geburtstag.csv to your computer, directly in a folder called daten in our project directory

The readr package

• we now have to read in the data

• we have to use a function that reads CSV data, and to specify where the data is in our RProject folder

• the readr package (part of tidyverse) can load in most data types, and has multiple functions for different data types

The here package

• how did R know exactly where to find the daten folder?
• our working directory is set to the location of our RProject within our computer
  • whenever we want to access data in our RProject, we should nest our filepath in here()
• to see where here() is starting from, run here()

here()

[1] "/Users/danielapalleschi/Documents/IdSL/Teaching/WiSe2324/B.A./r4ling"

• the output will look different for all of our machines, because it’s relative to where we placed our project folder

Working with data

Missing values

• you might’ve noticed some NA or N/A values
  • N/A was written as text in one of our observations, and so R reads it as such
  • NAs in R refer to missing data (“Not Available”)
  • true missing values are completely empty, so having N/A written in our df_groesse data is not actually read as a missing value
  • to fix this, we can use the argument na = for the read_csv() function, which tells read_csv() which values it should equate with missing values

Column names

• one column name in our data is surrounded by backticks (e.g., `Monat der Geburt`)
  • this is because it contains an empty space, which is not syntactically valid
  • a quick fix is to the function clean_names() from the janitor package, which we’ve already loaded in

Pipes

• pipes are placed at the end of function call when the result of this function should be passed through a subsequent function. Pipes can be read as “and then…”.

read_csv(here::here("daten", "groesse_geburtstag.csv")) |>
  head()

# A tibble: 3 × 5
  Haustier Größe `Monat der Geburt`   Tag L1      
  <chr>    <dbl>              <dbl> <dbl> <chr>   
1 Lola       171                  5     7 Englisch
2 <NA>       168                 11    26 Deutsch 
3 N/A        182                  4    15 Deutsch 

Other file types

• readr has other functions which are also easy to use, you just have to know when to use which ones

• read_csv2() reads semicolon-separated csv files (;)

  • this file type is common in countries that use , as the decimal marker (like Germany)

• read_tsv() reads tab-delimited files

• read_delim() function reads in files with any delimiter

  • it will try to guess the delimiter unless you specify it with the argument delim = (e.g., read_delim(groesse_geburtstag.csv, delim = ","))

Working with variables

• the major columns types to know are numerical and factor (categorical)
• factors contain categories or groups of data, but can sometimes look like numerical data
  • for example, our column month contains numbers, but it could also contain the name of each month
  • a good way to know which is which: it makes sense to calculate the mean of a numerical variable, but not of a factor
  • for example, it makes sense to calculate our average height, but not our average birth month